



import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 						lowercase		(					_A					,       _A):






	"""simple docstring"""


	@register_to_config
	def __init__(      self      :		Dict		,    *,
	    __UpperCAmelCase      :		int = 4		,    __UpperCAmelCase      :		int = 768		,    __UpperCAmelCase      :		int		,    __UpperCAmelCase      :		List[Any]		,    )     ->					Optional[Any]:
							super().__init__()

							UpperCAmelCase_=  nn.Parameter(torch.zeros(UpperCamelCase__	)	)

							# parameters for additional clip time embeddings
							UpperCAmelCase_=  nn.Linear(UpperCamelCase__		,    UpperCamelCase__	)
							UpperCAmelCase_=  nn.Linear(UpperCamelCase__		,    UpperCamelCase__	)

							# parameters for encoder hidden states
							UpperCAmelCase_=  clip_extra_context_tokens
							UpperCAmelCase_=  nn.Linear(
							    UpperCamelCase__		,    self.clip_extra_context_tokens * cross_attention_dim	)
							UpperCAmelCase_=  nn.Linear(UpperCamelCase__		,    UpperCamelCase__	)
							UpperCAmelCase_=  nn.LayerNorm(UpperCamelCase__	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		str		,    *, __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		List[Any]		,    __UpperCAmelCase      :		int	)     ->					List[Any]:






							if do_classifier_free_guidance:
													# Add the classifier free guidance embeddings to the image embeddings
													UpperCAmelCase_=  image_embeddings.shape[0]
													UpperCAmelCase_=  self.learned_classifier_free_guidance_embeddings.unsqueeze(0	)
													UpperCAmelCase_=  classifier_free_guidance_embeddings.expand(
													    UpperCamelCase__		,    -1	)
													UpperCAmelCase_=  torch.cat([classifier_free_guidance_embeddings, image_embeddings]		,    dim=0	)

							# The image embeddings batch size and the text embeddings batch size are equal
							assert image_embeddings.shape[0] == prompt_embeds.shape[0]

							UpperCAmelCase_=  prompt_embeds.shape[0]

							# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
							# adding CLIP embeddings to the existing timestep embedding, ...
							UpperCAmelCase_=  self.embedding_proj(UpperCamelCase__	)
							UpperCAmelCase_=  self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase__	)
							UpperCAmelCase_=  time_projected_image_embeddings + time_projected_prompt_embeds

							# ... and by projecting CLIP embeddings into four
							# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
							UpperCAmelCase_=  self.clip_extra_context_tokens_proj(UpperCamelCase__	)
							UpperCAmelCase_=  clip_extra_context_tokens.reshape(UpperCamelCase__		,    -1		,    self.clip_extra_context_tokens	)
							UpperCAmelCase_=  clip_extra_context_tokens.permute(0		,    2		,    1	)

							UpperCAmelCase_=  self.encoder_hidden_states_proj(UpperCamelCase__	)
							UpperCAmelCase_=  self.text_encoder_hidden_states_norm(UpperCamelCase__	)
							UpperCAmelCase_=  torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]		,    dim=1	)

							return text_encoder_hidden_states, additive_clip_time_embeddings






def a__       (			A_   ):






  '''simple docstring'''





  if len(A_   ) < 2:
    return collection

  def circle_sort_util(A_,	A_,	A_   ) -> bool:
    __magic_name__								=			False

    if low == high:
      return swapped

    __magic_name__								=			low
    __magic_name__								=			high

    while left < right:
      if collection[left] > collection[right]:
        __magic_name__	,       __magic_name__								=			(
            collection[right],
            collection[left],
        )
        __magic_name__								=			True

      left += 1
      right -= 1

    if left == right and collection[left] > collection[right + 1]:
      __magic_name__	,       __magic_name__								=			(
          collection[right + 1],
          collection[left],
      )

      __magic_name__								=			True

    __magic_name__								=			low + int((high - low) / 2   )
    __magic_name__								=			circle_sort_util(A_,	A_,	A_   )
    __magic_name__								=			circle_sort_util(A_,	mid + 1,	A_   )

    return swapped or left_swap or right_swap

  __magic_name__								=			True

  while is_not_sorted is True:
    __magic_name__								=			circle_sort_util(A_,	0,	len(A_   ) - 1   )

  return collection


if __name__ == "__main__":
    __lowerCAmelCase     :	Union[str, Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	Tuple       			=  [int(item) for item in user_input.split(',')]
    print(circle_sort(unsorted))

